Lithium-ion Battery Manufacturing Research Articles

Multi-model feature aggregation for classification of laser welding images with vision transformer

The laser welding technique is quite common in various manufacturing lines, including those for lithium-ion power batteries, due to its remarkable productivity, effectiveness, and flexibility. However, it has been observed that the consistency of the welding quality is not always optimal. This study investigates the adoption of laser welding in the manufacturing of lithium-ion batteries, such as their anode, cathode and safety vent, where consistent weld quality is crucial for battery performance and safety. Therefore, evaluating the laser-welding product is indispensable for industrial production and the public domain. Several techniques have been utilized for laser welding evaluation, both destructive and non-destructive. However, these methods are ineffective and too cumbersome to be adopted in mass manufacturing. In contrast, a machine vision strategy has recently been adopted to distinguish between successful and unsuccessful laser welding items. This opened new perspectives for evaluating the quality of the laser welding product using digital image techniques. However, these methods cannot deliver outstanding performance across multiple laser-welding products and achieve optimal classification accuracy. Deep learning has evolved remarkably in recent years and gained popularity in detecting welding defects. This paper presents the observation of a Hybrid Vision Transformer (HViT) to classify the laser welding images corresponding to their feature patch images based on multi-model feature aggregation. The VGG-16 and MobileNet, which were pre-trained on ImageNet, were utilized as the core models to extract the rich features of the laser welding image. To integrate these features, the squeeze excitation module (SE) was utilized, and a multi-layer perception (MLP) approach with a label smoothing optimizer was used for classification. To determine the effectiveness of the proposed strategy, a comparative analysis was conducted with a multitude of alternative machine learning and deep learning approaches. The results indicate that the proposed strategy surpasses all other methods in terms of classification accuracy and evaluation metrics on the test dataset.

Coating process control in lithium-ion battery manufacturing using cumulative sum charts

Coating process control in lithium-ion battery manufacturing using cumulative sum charts

Mechanical and Electrochemical Implications of Drying Temperature on Lithium‐Ion Battery Electrodes

Lithium‐ion battery (LIB) electrodes are typically produced with n‐methyl‐2‐pyrrolidone, a toxic solvent that is a known carcinogen and reproductive hazard. Accordingly, aqueous processing has been an expanding area of research interest in the field of LIB manufacturing. Although aqueous processing has been widely successful in anode processing, serious challenges remain in processing the cathode. In this work, the drying mechanics of cathode processed with both solvents is investigated though implementation of a chemical‐engineering‐based model to better understand the utilization of heat provided by experimentally determining the heat and mass transfer coefficients. Electrochemical performance is also evaluated to determine the impact of drying temperature on cycling performance. Binder distribution is determined via various methods to confirm differences in binder homogeneity as a function of both solvent and drying temperature. Identified is the large difference in the efficiency in which the heat is used as well as an ideal drying temperature for both aqueous and non‐aqueous processed cathodes. Also identified is the increased sensitivity to processing temperature for aqueous processed electrodes compared to non‐aqueous processed counterparts, pointing to the possibility of tuned drying regimes which would capitalize on the potential cost savings of aqueous processing for cathodes.

Residual NMP and Its Impacts on Performance of Lithium-Ion Cells

Abstract N-methyl-2-pyrrolidone (NMP) is the most common solvent used in coating positive electrode materials on aluminum foil during the manufacturing of lithium-ion batteries. NMP is a strongly polar aprotic solvent that effectively dissolves the polyvinylidene difluoride binder. While the majority of NMP typically evaporates during the electrode baking process, trace amounts may persist, particularly in positive electrodes containing nano-sized and highly-porous active materials. We noted residual NMP in the positive electrodes of Li-ion pouch cells containing LiMn0.8Fe0.2PO4 due to the extremely high surface area of the material and we wanted to determine the impact of this residual NMP. Therefore, a control electrolyte was purposely spiked with varying amounts of NMP and used in NMC532/graphite pouch cells to investigate the impact of residual NMP on lithium-ion battery performance. Experimental results indicate that NMP has the potential not only to neutralize the electrolyte additive ethylene sulfate but also to independently increase cathode impedance, leading to a higher rate of capacity loss during charge-discharge cycling. It is crucial to establish the appropriate procedure for baking electrodes containing NMP, both in laboratory and industrial settings, to mitigate these effects.

Investigation of the influence of cell compression after winding on electrolyte wettability based on experiments and lattice Boltzmann simulation

The wetting of the active materials in a lithium-ion battery cell after electrolyte filling is a time-critical process in the manufacturing of lithium-ion batteries. The exact influencing factors on wetting are the subject of experimental investigations and numerical simulations. The dominant wetting at the material transitions, depending on the material’s compression caused by the cell winding, has not yet been examined. Therefore, this publication investigates the effects of material compression during the winding process on wettability through experiments and lattice Boltzmann simulations. The results show and quantify accelerated wetting caused in the material transitions compared to single electrode wetting, attributed to larger pores and higher permeability. Increasing compression in inner windings of cylindrical cells shows smaller pores sizes and reduced permeability, leading to a decelerated wetting behavior. Considering permeability and evaporation is essential for the mathematical replication of the wetting phenomena. With these findings, the total wetting time of a winding in a cylindrical 21700 cell can be estimated between 816 s to 1198 s.

Photocatalytic oxidation degradability of ammonia–nitrogen and Ni/Co ammonia complexes in ternary precursor wastewater by constructing MoS2/g-C3N4 heterojunction: Performance and mechanism

In this study, MoS2/g-C3N4 heterojunction photocatalysts were fabricated in order to treat ammonia–nitrogen wastewater resulting from the production of ternary cathode material precursors used in the manufacture of lithium-ion batteries. The catalysts were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), N2 adsorption–desorption test, UV–Vis diffuse reflectance spectra (UV–Vis DRS), Fourier-transform infrared spectroscopy (FT-IR Nicolet 6700), energy dispersive X-ray spectroscopy (EDS), and X-ray photoelectron spectroscopy (XPS). The catalytic efficiency of the catalysts on ammonia–nitrogen and Ni/Co ammonia complexes in the wastewater was investigated by varying MoS2 doping, catalyst dose, adsorption duration, and photocatalysis duration. The results showed 2.0 g/L of MoS2/g-C3N4-1 wt% (photocatalyst with a content of 1 wt% MoS2) has the best photocatalytic efficiency. A degradation efficiency of 87.52 % for ammonia–nitrogen, 98.34 % for nickel and 97.80 % for cobalt was reached after one hour of dark adsorption and four hours of illumination. EDS and XPS analysis revealed that Ni2+/Ni3+ and Co2+/Co3+ were adsorbed from the solution onto the catalyst surface, and that NH4+/NH3 was converted into NO3− and N2.

Safety Analysis of Lithium-Ion Cylindrical Batteries Using Design and Process Failure Mode and Effect Analysis

Cylindrical lithium-ion batteries are widely used in consumer electronics, electric vehicles, and energy storage applications. However, safety risks due to thermal runaway-induced fire and explosions have prompted the need for safety analysis methodologies. Though cylindrical batteries often incorporate safety devices, the safety of the battery also depends on its design and manufacturing processes. This study conducts a design and process failure mode and effect analysis (DFMEA and PFMEA) for the design and manufacturing of cylindrical lithium-ion batteries, with a focus on battery safety.

Enhancing Li+ recovery in brine mining: integrating next-gen emotional AI and explainable ML to predict adsorption energy in crown ether-based hierarchical nanomaterials.

Artificial intelligence (AI) is being employed in brine mining to enhance the extraction of lithium, vital for the manufacturing of lithium-ion batteries, through improved recovery efficiencies and the reduction of energy consumption. An innovative approach was proposed combining Emotional Neural Networks (ENN) and Random Forest (RF) algorithms to elucidate the adsorption energy (AE) (kcal mol-1) of Li+ ions by utilizing crown ether (CE)-incorporated honeycomb 2D nanomaterials. The screening and feature engineering analysis of honeycomb-patterned 2D materials and individual CE were conducted through Density Functional Theory (DFT) and Gaussian 16 simulations. The selected honeycomb-patterned 2D materials encompass graphene, silicene, and hexagonal boron nitride, while the specific CEs evaluated are 15-crown-5 and 18-crown-6. The crown-passivated 2D surfaces held a significant adsorption site through van der Waals forces for efficient recovery of Li+ ions. ENN predicted the targeted adsorption sites with high precision and minimal deviation. The eTAI (XAI) based Shapley Additive exPlanations (SHAP) was also explored for insight into the feature importance of CE embedded 2D nanomaterials for the recovery of Li+ ions. The extreme gradient boosting algorithm (XGBoost) model demonstrated a RT-2-MAPE = 0.4618% and ENN-2-MAPE = 0.4839% for the feature engineering analysis. This research would be an insight into the AI-driven nanotechnology that presents a viable and sustainable approach for the extraction of natural resources through the application of brine mining.

A comprehensive bi-objective optimization model to design circular supply chain networks for sustainable electric vehicle batteries

<abstract> <p>As electric vehicles (EVs) continue to advance, there is a growing emphasis on sustainability, particularly in the area of effectively managing the lifecycle of EV batteries. In this study, an efficient and novel optimization model was proposed for designing a circular supply chain network for EV batteries. In doing so, a comprehensive, bi-objective, mixed-integer linear programming model was employed. It is worth noting that the current model outlined in this paper involved both forward and reverse flows, illustrating the process of converting used batteries into their constituent materials or repurposing them for various applications. In line with the circular economy concept, the current model also minimized the total costs and carbon emission to develop an inclusive optimization framework. The LP-metric method was applied to solve the presented bi-objective optimization model. We simulated six problems with different sizes using data and experts' knowledge of a lithium-ion battery manufacturing industry in Canada, and evaluated the performance of the proposed model by simulated data. The results of the sensitivity analysis process of the objective functions coefficients showed that there was a balance between the two objective functions, and the costs should be increased to achieve lower emissions. In addition, the demand sensitivity analysis revealed that the increase in demand directly affects the increase in costs and emissions.</p> </abstract>

Quantifying the Lithium Loss in the Graphite Anodes of Commercial Batteries

Loss of Li-ions, which results in capacity loss, mainly occurs on the negative electrode in the form of Li plating or a surface film. Quantifying the loss of lithium in graphite anode is essential for studies such as waste battery recycling, lithium plating on negative electrodes, and interfacial film composition, which can guide the manufacturing and application chain of commercial lithium-ion batteries.Various methods for quantifying lithium losses have been proposed. You et al.[1] proposed the use of inductively coupled plasma (ICP) method to quantify lithium loss in graphite. This is a low-cost method that is easy to apply on a large scale, but it requires large sample amount and high instrumentation precision. Fang et al. [2] used water titration in conjunction with gas chromatography to quantify lithium loss from lithium metal batteries. This method is more subtle, but the produced trace amounts of gas are often too difficult to collect. In this study, we used water to quantify the lithium loss in graphite based on the method of Fang et al. We then combined pH and X-ray photoelectron spectroscopy (XPS) analysis to quantify the total lithium loss in graphite.To perform the quantify method, we washed the middle part of the negative electrodes of fresh and aged cells with DEC. We then cut out 2 cm² the negative electrodes and placed them in two volumetric flasks filled with 20 mL of deionized water each. After stirring, we sealed the flasks and placed them in a 60 °C oven for a week to ensure that the lithium compounds were fully reacted and dissolved with water. We added BaCl2 to remove CO3 2- and F- ions, which can hydrolyze to produce OH- and affect pH. We then performed a pH test to quantify the LiOH content in solution. As reported, LiOH in solution is mainly from Li and Li2O compounds[2], so we were able to quantify the Li in graphite in the form of Li/Li2O.To analyze the surface composition of graphite materials, we conducted an XPS test. We quantified the total lithium loss based on the percentage of interfacial lithium compounds occupied by Li/Li-O. It should be noted that this quantification method is based on the assumption that the surface material is homogeneous. There are three fitted peaks for the aged negative electrode, which belong to LiF, Li2CO3, and Li/Li–O compounds, corresponding to an amount of 73.66%, 19.79%, and 6.55% Li, respectively. Here, the Li/Li–O compounds correspond to the pH quantified part (1.10%). Therefore, the lithium loss in the aged negative electrode can be quantified as 16.82%.In conclusion, we proposed a water reaction method to quantify the loss of lithium in graphite, which was then combined with pH and XPS analysis to quantify the total lithium loss. This method offers a subtle yet effective way to quantify lithium loss. By quantifying the total lithium loss, we can gain insights into the interfacial film composition, lithium plating on negative electrodes, and waste battery recycling, which can guide the manufacturing and application of commercial lithium-ion batteries.Reference[1] H. You, B. Jiang, J. Zhu, X. Wang, G. Shi, G. Han, X. Wei, H. Dai, Journal of Power Sources 2023, 564, 232892.[2] C. Fang, J. Li, M. Zhang, Y. Zhang, F. Yang, J. Z. Lee, M. H. Lee, J. Alvarado, M. A. Schroeder, Y. Yang, B. Lu, N. Williams, M. Ceja, L. Yang, M. Cai, J. Gu, K. Xu, X. Wang, Y. S. Meng, Nature 2019, 572, 511.[3] J. Guo, S. Jin, X. Sui, X. Huang, Y. Xu, Y. Li, D. W. Peter Kjær Kristensen, D.-I. Stroe, Journal of Materials Chemistry A 2023.

On-site measurement and optimization of energy efficiency of the cascading desiccant wheel deep dehumidification systems in the lithium-ion battery manufacturing factory

The desiccant wheel deep dehumidification system in the lithium-ion battery manufacturing factory is significantly energy intensive, which substantially treats the process air from the ambient to the supply air state with a dew point temperature below −20 °C. On-site measurements were conducted in this study to investigate the actual performance of the deep dehumidification systems. The air treatment process and energy consumption during the operation were continuously monitored. The results reveal that the coefficient of performance of the dehumidification process of the systems ranges from 0.54 to 0.63 under basic winter conditions and ranges from 1.15 to 1.25 under basic summer conditions. The deep dehumidification processes of the systems are divided into multiple stages, and with the deepening of the process air humidity ratio from above 6 g/kg to below 0.3 g/kg, the partial energy efficiency of the treatment stages decreases. The efficient operational strategies are further elaborated for the improvement of the deep dehumidification system, including the proper match between the dehumidification gradient and the dehumidification stages, and the appropriate use of outlet process air with low humidity ratio as the regeneration air. The energy consumption of the cascading desiccant wheel dehumidification system can be reduced by lowering the cooling temperature of the cooling coil to undertake more latent load in the preliminary dehumidification stage. The effect of the bypass air state as the regeneration air is revealed, and the energy efficiency of the system is enhanced by up to 32 % by applying appropriate bypass air. This study offers guidance for the future design of the cascading desiccant wheel deep dehumidification system in similar applications.

Surface Engineering Based on Conductive Agent Dispersion Uniformity: Strategies toward Performance Consistency of Lithium-Ion Batteries.

The consistency of lithium-ion battery performance is the key factor affecting the safety and cycle life of battery packs. Surface engineering of electrodes in production processes plays an important role in improving the consistency of battery performance. In this study, the drying process in the electrode manufacturing process is studied as the effect on surface engineering of the electrode materials, with consideration on impacting the battery performance. Specifically, the solid content of the slurry and drying temperature are considered to be the two factors that affect conductive agent dispersion uniformity in the porous electrodes. To achieve surface engineering on the dispersion uniformity of the conductive agent, the optimal processing parameters can be obtained by adjusting the temperature and solid content of the slurry. The mechanism of dispersion uniformity of the conductive agent is mainly related to the polyvinylidene fluoride grid structure. In the manufacturing of lithium-ion batteries, the electrode coated with 66% solid slurry and dried at 90-100 °C presents stable energy storage performance, which is beneficial to maintain the stable performance of the battery pack in the application.

Development of a High-Frequency Test System to Study the Wear of Ultrasonic Welding Tools

In current automotive lithium-ion battery manufacturing, Ultrasonic Metal Welding (USMW) is one of the major joining techniques due to its advantages in welding multiple thin sheets of highly conductive materials. The sonotrode, serving as the welding tool, transmits high-frequency oscillation to the joining parts. Due to the high frequency of thermal-mechanical loading, the knurl pattern on the sonotrode wears with an increasing number of welds, which significantly influences the welding process, resulting in poor joint quality. In this study, a high-frequency test system was developed to investigate the wear mechanisms of the sonotrode. Based on the comparable relative motion to the welding process, the thermal-mechanical loadings on the contact area were analyzed. As the oscillation amplitude of the sonotrode increased, the estimated frictional force between the sonotrode and the copper counter body remained constant, while an increase in the sliding distance was observed in the contact area. Temperature development showed a strong correlation with mechanical loading. A first approach of continuous testing was performed but was limited due to the failure of the copper counter body under ultrasonic stimulation.

A Polytetrafluoroethylene-Based Solvent-Free Procedure for the Manufacturing of Lithium-Ion Batteries.

Lithium-ion batteries (LIBs) have recently become popular for energy storage due to their high energy density, storage capacity, and long-term cycle life. Although binders make up only a small proportion of LIBs, they have become the key to promoting the transformation of the battery preparation process. Along with the development of binders, the battery manufacturing process has evolved from the conventional slurry-casting (SC) process to a more attractive solvent-free (SF) method. Compared with traditional LIBs manufacturing method, the SF method could dramatically reduce and increase the energy density due to the reduced preparation steps and enhanced electrode loading. Polytetrafluoroethylene (PTFE), as a typical binder, has played an important role in fabricating high-performance LIBs, particularly in regards to the SF technique. In this paper, the development history and application status of PTFE binder was introduced, and then its contributions and the inherent problems involved in the SF process were described and analyzed. Finally, the viewpoints concerning the future trends for PTFE-based SF manufacturing methods were also discussed. We hope this work can inspire future research concerning high-quality SF binders and assist in promoting the evolution of the SF manufacturing technology in regards to LIBs.

Lithium-Ion Battery Manufacturing: Industrial View on Processing Challenges, Possible Solutions and Recent Advances

Developments in different battery chemistries and cell formats play a vital role in the final performance of the batteries found in the market. However, battery manufacturing process steps and their product quality are also important parameters affecting the final products’ operational lifetime and durability. In this review paper, we have provided an in-depth understanding of lithium-ion battery manufacturing in a chemistry-neutral approach starting with a brief overview of existing Li-ion battery manufacturing processes and developing a critical opinion of future prospectives, including key aspects such as digitalization, upcoming manufacturing technologies and their scale-up potential. In this sense, the review paper will promote an understanding of the process parameters and product quality.

Evaluation of ultrasonic spot welded Al-Cu dissimilar sheets: Mechanical performance, microstructural changes and thermal analysis

Automotive lithium-ion battery manufacturing industries are increasingly using ultrasonic welding (USW) to join thin dissimilar metals and foils. USW has good joint efficiency, no formation of the heat-affected zone, high production rate, shorter welding time, and sustainability over conventional fusion welding techniques. However, this process is less distinct and precise control of weld surface conditions to get good quality welds. In this regard, experimental and thermal analysis significantly helps to control the state of the weld interface during the joining process. In this study, the objective is to enhance the mechanical performance of aluminum (Al) and copper (Cu) joints under different surface conditions such as lubricating, normal, electrolytically, and emery polished. According to the lap shear tensile load result, the joint strength decreases significantly with the rise in weld time after 0.7 s. The lubricating surface provides the maximum lap shear tensile load of 1507 N among all other surface conditions. Fracture surface analyses are conducted to determine the quality of welds under varying surface conditions. Additionally, a three-dimensional (3D) finite element model (FE) for ultrasonic welding of two highly conductive metal sheets is also being investigated for the purposes of analysing the thermal behaviour around the weld interface. It reveals that the thermal profile of the interface differs depending on the surface condition. The numerical framework is confirmed by comparing the predicted interface temperature variations at different faying surface conditions with the experimental results.

Fast grading method based on data driven capacity prediction for high-efficient lithium-ion battery manufacturing

With the large-scale expansion of the battery market, the cost optimization of battery manufacturing has become a focus of attention. Among the complex production process of the battery, capacity grading requires a full discharge to measure the capacity and results in high cost. This study proposes a fast grading method in which the batteries are half discharged and graded according to the capacity predicted by a neural network. The prediction-based method takes half the time and saves about 37 % of the energy consumption. Twenty-three features are extracted as the initial features. The collinear features are screened-out, and three feature reduction methods are compared. Permutation importance can effectively clarify the nonlinear relationship and determine the critical features. The root-mean-square error of the testing set is 0.18 %. The method is feasible and has the potential for further evolution. The electrochemical mechanism involved in the features is analyzed by simulation. Under the guidance of the mechanism, the method can be transferred to other batteries.

Lithium, Brexit and Global Britain: Onshoring battery production networks in the UK

As demand for electrical energy storage scales, production networks for lithium-ion battery manufacturing are being re-worked organisationally and geographically. The UK - like the US and EU - is seeking to onshore lithium-ion battery production and build a national battery supply chain. Governmental, industrial and research actors are engaged in securing battery mineral materials and developing battery manufacturing capacity, in the context of the country's exit from the EU and a perceived ‘global battery race’ in which geopolitical goals shape links with new and old partners. We identify the primary global networks of lithium mining and refining, battery chemical production, technology development and finance in which the UK's battery manufacturing capacity are increasingly embedded. We foreground the role of the UK state, and how it has sought to assemble discrete capacities in automobile manufacturing, battery R&D, materials chemistry, minerals exploration, mining and green finance into a national battery sector. We mobilise a Global Production Network (GPN) perspective to highlight the cross-border geographical and organisational structures through which onshoring is taking place. We extend GPN research on the role of the state by showing how the UK's growing lithium networks intersect with a plural and differentiated state accumulation project of green industrial transformation. We outline the selective nature of this state accumulation project, highlight instances of coupling creation as the state seeks to strategically couple regional assets with firms in GPNs, and point to a convergence of industrial and innovation policy characteristic of the entrepreneurial state.

Judgment Basis and Mechanical Analysis of Current Collector Failure in the Winding Process of a Lithium-Ion Battery

The winding process is one of the essential processes in the manufacturing of lithium-ion batteries (LIBs). Current collector failure frequently occurs in the winding process, which severely increases the production cost and reduces production efficiency. In order to solve this problem, we first analyze the relationship between different process parameters and the failure of the current collector, and put forward the standard to determine the failure of the current collector. Moreover, we conducted tensile experiments to validate the differences in the mechanical performance of the current collector under different thicknesses. Finally, the circumferential stress and strain of the current collector winding were calculated using finite element analysis. The accuracy of the proposed criterion for determining current collector failure was verified through experimental measurements of stress and strain. The results demonstrate that the criterion proposed in this study can accurately calculate the maximum stress during the current collector winding process, providing a powerful tool for addressing the issue of current collector failure in the winding process.

Multi-Objective Optimization of Kinetic Characteristics for the LBPRM-EHSPCS System

As the ‘heart’ of energy vehicles, the lithium-ion battery is in desperate need of precision improvement, green production, and cost reduction. To achieve this goal, the electro-hydraulic servo pump control system (EHSPCS) is applied to the lithium-ion battery pole rolling mill (LBPRM). However, this development can lead to limited dynamic performance and large power loss as a result of the EHSPCS unique volume direct-drive control mode. At present, how to solve this conflict has not been studied and how the EHSPCS component parameters influence the dynamic response, power loss, and economic performance is not clear. In this paper, a multi-objective optimization (MOO) model for the LBPRM-EHSPCS is proposed by comprehensively considering the dynamic, efficiency, and economic characteristics. Firstly, the evaluation model of the dynamic response, power loss, and cost is investigated. Then, the NSGA-II algorithm is introduced to address the Pareto front of the MOO model. Finally, the power loss and dynamic response of the LBPRM-EHSPCS before and after optimization are tested to validate the viability of the raised method. Results indicate that power loss is decreased by as much as 7.2% while steady-state precision is greatly improved after optimization. The proposed framework enhances the performance in lithium-ion battery manufacturing and can be applied to other kinds of hydraulic systems.